Instead of evaluating models with the typical leaderboard setup with fixed (train/dev/test) splits on a specific target domain, it would be also important to test models in different scenarios, so that different aspects of the model can be captured. For example, as neural models are data hungry, more work is still needed on transfer learning scenarios, where evaluation is carried out with less or without labeled data (zero-shot) for a particular target domain. In addition, most models for SF and IC are evaluated on English, which means that more effort is still needed to make models that work well for other languages. Some recent works have started exploring zero-shot cross lingual methods [1]}, [2]}, [3]} and also few-shot scenarios [4]} and the room for improvement for these scenarios is still large. In short, designing a data efficient model that is portable across domains and languages is still a challenging problem for the coming future.
